{
 "cells": [
  {
   "cell_type": "markdown",
   "id": "3d0efed1-eca7-4470-bed1-a2242b509c12",
   "metadata": {},
   "source": [
    "# Univariate Time Series Forecasting with xgb-cv"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 1,
   "id": "fa5719e3-a811-4bd8-9119-0c8f85ea3b09",
   "metadata": {},
   "outputs": [],
   "source": [
    "import numpy as np\n",
    "import pandas as pd\n",
    "import matplotlib.pyplot as plt\n",
    "import matplotlib.dates as mdates\n",
    "import seaborn as sns\n",
    "\n",
    "from xgboost import XGBRegressor\n",
    "from sklearn.preprocessing import MinMaxScaler\n",
    "from sklearn.model_selection import TimeSeriesSplit, GridSearchCV\n",
    "from sklearn.metrics import mean_absolute_error, \\\n",
    "                            mean_absolute_percentage_error, \\\n",
    "                            mean_squared_error, root_mean_squared_error, \\\n",
    "                            r2_score\n",
    "\n",
    "np.random.seed(0)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 2,
   "id": "cd8d0855-d83c-44ed-93e1-973169d82e0e",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style type=\"text/css\">\n",
       "#T_c56f7_row0_col1, #T_c56f7_row0_col2, #T_c56f7_row0_col3, #T_c56f7_row0_col4, #T_c56f7_row0_col5, #T_c56f7_row2_col6 {\n",
       "  background-color: #023858;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c56f7_row0_col6 {\n",
       "  background-color: #bbc7e0;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row1_col1, #T_c56f7_row1_col3 {\n",
       "  background-color: #348ebf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c56f7_row1_col2 {\n",
       "  background-color: #67a4cc;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c56f7_row1_col4, #T_c56f7_row1_col5 {\n",
       "  background-color: #2081b9;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c56f7_row1_col6, #T_c56f7_row2_col2, #T_c56f7_row2_col3, #T_c56f7_row2_col4, #T_c56f7_row2_col5, #T_c56f7_row4_col1 {\n",
       "  background-color: #fff7fb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row2_col1, #T_c56f7_row3_col2, #T_c56f7_row3_col6 {\n",
       "  background-color: #dbdaeb;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row3_col1 {\n",
       "  background-color: #f9f2f8;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row3_col3 {\n",
       "  background-color: #e0deed;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row3_col4, #T_c56f7_row3_col5 {\n",
       "  background-color: #c6cce3;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row4_col2 {\n",
       "  background-color: #e6e2ef;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row4_col3 {\n",
       "  background-color: #d9d8ea;\n",
       "  color: #000000;\n",
       "}\n",
       "#T_c56f7_row4_col4, #T_c56f7_row4_col5 {\n",
       "  background-color: #328dbf;\n",
       "  color: #f1f1f1;\n",
       "}\n",
       "#T_c56f7_row4_col6 {\n",
       "  background-color: #d3d4e7;\n",
       "  color: #000000;\n",
       "}\n",
       "</style>\n",
       "<table id=\"T_c56f7\">\n",
       "  <thead>\n",
       "    <tr>\n",
       "      <th class=\"blank level0\" >&nbsp;</th>\n",
       "      <th id=\"T_c56f7_level0_col0\" class=\"col_heading level0 col0\" >Date</th>\n",
       "      <th id=\"T_c56f7_level0_col1\" class=\"col_heading level0 col1\" >Open</th>\n",
       "      <th id=\"T_c56f7_level0_col2\" class=\"col_heading level0 col2\" >High</th>\n",
       "      <th id=\"T_c56f7_level0_col3\" class=\"col_heading level0 col3\" >Low</th>\n",
       "      <th id=\"T_c56f7_level0_col4\" class=\"col_heading level0 col4\" >Close</th>\n",
       "      <th id=\"T_c56f7_level0_col5\" class=\"col_heading level0 col5\" >Adj Close</th>\n",
       "      <th id=\"T_c56f7_level0_col6\" class=\"col_heading level0 col6\" >Volume</th>\n",
       "    </tr>\n",
       "  </thead>\n",
       "  <tbody>\n",
       "    <tr>\n",
       "      <th id=\"T_c56f7_level0_row0\" class=\"row_heading level0 row0\" >10926</th>\n",
       "      <td id=\"T_c56f7_row0_col0\" class=\"data row0 col0\" >2024-04-17</td>\n",
       "      <td id=\"T_c56f7_row0_col1\" class=\"data row0 col1\" >169.610001</td>\n",
       "      <td id=\"T_c56f7_row0_col2\" class=\"data row0 col2\" >170.649994</td>\n",
       "      <td id=\"T_c56f7_row0_col3\" class=\"data row0 col3\" >168.000000</td>\n",
       "      <td id=\"T_c56f7_row0_col4\" class=\"data row0 col4\" >168.000000</td>\n",
       "      <td id=\"T_c56f7_row0_col5\" class=\"data row0 col5\" >168.000000</td>\n",
       "      <td id=\"T_c56f7_row0_col6\" class=\"data row0 col6\" >50901200</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c56f7_level0_row1\" class=\"row_heading level0 row1\" >10927</th>\n",
       "      <td id=\"T_c56f7_row1_col0\" class=\"data row1 col0\" >2024-04-18</td>\n",
       "      <td id=\"T_c56f7_row1_col1\" class=\"data row1 col1\" >168.029999</td>\n",
       "      <td id=\"T_c56f7_row1_col2\" class=\"data row1 col2\" >168.639999</td>\n",
       "      <td id=\"T_c56f7_row1_col3\" class=\"data row1 col3\" >166.550003</td>\n",
       "      <td id=\"T_c56f7_row1_col4\" class=\"data row1 col4\" >167.039993</td>\n",
       "      <td id=\"T_c56f7_row1_col5\" class=\"data row1 col5\" >167.039993</td>\n",
       "      <td id=\"T_c56f7_row1_col6\" class=\"data row1 col6\" >43122900</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c56f7_level0_row2\" class=\"row_heading level0 row2\" >10928</th>\n",
       "      <td id=\"T_c56f7_row2_col0\" class=\"data row2 col0\" >2024-04-19</td>\n",
       "      <td id=\"T_c56f7_row2_col1\" class=\"data row2 col1\" >166.210007</td>\n",
       "      <td id=\"T_c56f7_row2_col2\" class=\"data row2 col2\" >166.399994</td>\n",
       "      <td id=\"T_c56f7_row2_col3\" class=\"data row2 col3\" >164.080002</td>\n",
       "      <td id=\"T_c56f7_row2_col4\" class=\"data row2 col4\" >165.000000</td>\n",
       "      <td id=\"T_c56f7_row2_col5\" class=\"data row2 col5\" >165.000000</td>\n",
       "      <td id=\"T_c56f7_row2_col6\" class=\"data row2 col6\" >67772100</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c56f7_level0_row3\" class=\"row_heading level0 row3\" >10929</th>\n",
       "      <td id=\"T_c56f7_row3_col0\" class=\"data row3 col0\" >2024-04-22</td>\n",
       "      <td id=\"T_c56f7_row3_col1\" class=\"data row3 col1\" >165.520004</td>\n",
       "      <td id=\"T_c56f7_row3_col2\" class=\"data row3 col2\" >167.259995</td>\n",
       "      <td id=\"T_c56f7_row3_col3\" class=\"data row3 col3\" >164.770004</td>\n",
       "      <td id=\"T_c56f7_row3_col4\" class=\"data row3 col4\" >165.839996</td>\n",
       "      <td id=\"T_c56f7_row3_col5\" class=\"data row3 col5\" >165.839996</td>\n",
       "      <td id=\"T_c56f7_row3_col6\" class=\"data row3 col6\" >48116400</td>\n",
       "    </tr>\n",
       "    <tr>\n",
       "      <th id=\"T_c56f7_level0_row4\" class=\"row_heading level0 row4\" >10930</th>\n",
       "      <td id=\"T_c56f7_row4_col0\" class=\"data row4 col0\" >2024-04-23</td>\n",
       "      <td id=\"T_c56f7_row4_col1\" class=\"data row4 col1\" >165.350006</td>\n",
       "      <td id=\"T_c56f7_row4_col2\" class=\"data row4 col2\" >167.050003</td>\n",
       "      <td id=\"T_c56f7_row4_col3\" class=\"data row4 col3\" >164.919998</td>\n",
       "      <td id=\"T_c56f7_row4_col4\" class=\"data row4 col4\" >166.899994</td>\n",
       "      <td id=\"T_c56f7_row4_col5\" class=\"data row4 col5\" >166.899994</td>\n",
       "      <td id=\"T_c56f7_row4_col6\" class=\"data row4 col6\" >48917700</td>\n",
       "    </tr>\n",
       "  </tbody>\n",
       "</table>\n"
      ],
      "text/plain": [
       "<pandas.io.formats.style.Styler at 0x27c8d5c62d0>"
      ]
     },
     "execution_count": 2,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data = pd.read_csv('AAPL.csv')\n",
    "data.tail(5).style.background_gradient()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9e060503-7135-46f2-a4a6-299a838cd718",
   "metadata": {},
   "source": [
    "**数据预处理**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 3,
   "id": "2afcb02b-53a3-498d-b608-5d2f0de8db71",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "<class 'numpy.float64'> <class 'str'>\n",
      "<class 'numpy.float64'> <class 'pandas._libs.tslibs.timestamps.Timestamp'>\n",
      "(755, 6)\n"
     ]
    }
   ],
   "source": [
    "print(type(data['Close'].iloc[0]),type(data['Date'].iloc[0]))\n",
    "# Let's convert the data type of timestamp column to datatime format\n",
    "data['Date'] = pd.to_datetime(data['Date'])\n",
    "print(type(data['Close'].iloc[0]),type(data['Date'].iloc[0]))\n",
    "\n",
    "# Selecting subset\n",
    "cond_1 = data['Date'] >= '2021-04-23 00:00:00'\n",
    "cond_2 = data['Date'] <= '2024-04-23 00:00:00'\n",
    "data = data[cond_1 & cond_2].set_index('Date')\n",
    "print(data.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "4782bd75-d717-4c91-a577-07901521989c",
   "metadata": {},
   "source": [
    "**数据可视化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 4,
   "id": "0714c4f4-b88a-4270-9e77-2ad2a933b1ba",
   "metadata": {},
   "outputs": [],
   "source": [
    "sns.set_style('whitegrid')\n",
    "# plt.style.use('seaborn-v0_8-whitegrid')"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 5,
   "id": "e74248e6-03cc-4b15-af95-f4c33976aa5a",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(figsize=(10, 6))\n",
    "\n",
    "# 绘制数据\n",
    "ax.plot(data['Close'], color='y' ,label='AAPL')\n",
    "\n",
    "# 设置x轴为时间轴，并显示具体日期\n",
    "locator = mdates.AutoDateLocator(minticks=8, maxticks=12)  # 自动定位刻度\n",
    "formatter = mdates.DateFormatter('%Y-%m-%d')  # 自定义刻度标签格式\n",
    "ax.xaxis.set_major_locator(locator)\n",
    "ax.xaxis.set_major_formatter(formatter)\n",
    "\n",
    "# 设置标题\n",
    "plt.title('Close Price History', fontdict={'family': 'Times New Roman', 'fontsize': 16})\n",
    "# 旋转刻度标签以提高可读性\n",
    "plt.xticks(rotation=45)\n",
    "\n",
    "plt.ylabel('Close Price USD ($)', fontdict={'family': 'Times New Roman', 'fontsize': 14})\n",
    "plt.legend(loc=\"upper right\", prop={'family': 'Times New Roman'})\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "1942f4ff-abf4-40b3-8b79-14ad7c183f26",
   "metadata": {},
   "source": [
    "**特征工程**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "19bad8e7-3c57-42ad-8687-49c9988a2095",
   "metadata": {},
   "source": [
    "**特征缩放——归一化**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 6,
   "id": "2343df6a-883d-497e-a6b7-a0930fc3cb33",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/plain": [
       "Index(['Open', 'High', 'Low', 'Close', 'Adj Close', 'Volume'], dtype='object')"
      ]
     },
     "execution_count": 6,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "data.columns"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 7,
   "id": "f0144055-c0f1-4600-b47f-4a0f85b4b33d",
   "metadata": {},
   "outputs": [],
   "source": [
    "values = data['Close'].values.reshape(-1, 1)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 8,
   "id": "c9b32433-7556-4418-b7a9-56f3b15dfec2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "(755, 1)\n"
     ]
    }
   ],
   "source": [
    "# 创建 MinMaxScaler实例，进行拟合和变换，生成NumPy数组\n",
    "scaler = MinMaxScaler()\n",
    "values_scaled = scaler.fit_transform(values)\n",
    "\n",
    "print(values_scaled.shape)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "6641cba3-2a9d-4413-a839-6a10e714359d",
   "metadata": {},
   "source": [
    "**构建监督学习数据**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 9,
   "id": "a53a60e9-a92e-4dc6-9dc4-c09d9251761f",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义滞后步数（即使用过去多少天的数据来预测未来的值）\n",
    "lag = 2\n",
    "X_list = []  # 初始化特征列表\n",
    "y_list = []  # 初始化目标列表\n",
    "\n",
    "# 遍历时间序列数据，生成监督学习数据\n",
    "for i in range(len(values_scaled) - lag):\n",
    "    # 提取当前行及其滞后行的数据，并展平数组\n",
    "    X_list.append(values_scaled[i:i+lag, :].flatten())  \n",
    "    # 提取目标行的目标值（这里我们使用下一个时间点的收盘价作为目标）\n",
    "    y_list.append(values_scaled[i+lag, -1])  # 提取收盘价作为目标值\n",
    "\n",
    "X = np.array(X_list) # [samples, num_features]\n",
    "y = np.array(y_list) # [target]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "00aa96d0-c5e7-4281-b3a8-969402e3711b",
   "metadata": {},
   "source": [
    "上述代码的目的是进行时间序列数据的预处理，将原始的时间序列数据转换为适合机器学习模型输入的监督学习数据格式。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "be463ccd-bf3b-436d-9b30-98f5b3d724c7",
   "metadata": {},
   "source": [
    "`lag`：表示每个样本中包含的滞后步数，它决定了模型在预测时考虑的历史数据长度。\n",
    "`X_list`：用于存储分割后的特征数据样本的列表。\n",
    "`y_list`：用于存储每个特征数据样本对应的目标值的列表。"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "12a1aae5-0177-4b6a-9eaf-4e2a41710dd7",
   "metadata": {},
   "source": [
    "**实例化模型、定义超参数空间**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 10,
   "id": "ee99c179-4e01-4aa7-90f1-5f9fb5582dfb",
   "metadata": {},
   "outputs": [],
   "source": [
    "model = XGBRegressor(\n",
    "    objective='reg:squarederror',\n",
    "    n_estimators=1000,\n",
    "    learning_rate=0.03,\n",
    "    eval_metric=['mae', 'rmse']  # 指定多个评估指标\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 11,
   "id": "4a534eaa-e639-4b97-b950-937e34a3a0bd",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 定义超参数的搜索空间\n",
    "param_grid = {\n",
    "    'n_estimators': [500, 800, 1000],\n",
    "    'learning_rate': [0.01, 0.03, 0.05],\n",
    "    'max_depth': [3, 5, 7]\n",
    "}"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "59ac44c6-dd6b-43a6-815b-e4d4cc94cbc6",
   "metadata": {},
   "source": [
    "**交叉验证与超参数调优**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 12,
   "id": "29c5a23f-60a0-4f21-ae60-7dcbf76ce9dc",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fitting 5 folds for each of 27 candidates, totalling 135 fits\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END .learning_rate=0.01, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.01, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END .learning_rate=0.01, max_depth=5, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.01, max_depth=5, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.01, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END .learning_rate=0.01, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END .learning_rate=0.01, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=500; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=500; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=500; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=800; total time=   1.5s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END ..learning_rate=0.01, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END .learning_rate=0.01, max_depth=7, n_estimators=1000; total time=   1.7s\n",
      "[CV] END .learning_rate=0.01, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END .learning_rate=0.01, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END .learning_rate=0.03, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=800; total time=   0.7s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.03, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END .learning_rate=0.03, max_depth=5, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=5, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END .learning_rate=0.03, max_depth=5, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=500; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=500; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=800; total time=   0.7s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=800; total time=   1.1s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END ..learning_rate=0.03, max_depth=7, n_estimators=800; total time=   1.4s\n",
      "[CV] END .learning_rate=0.03, max_depth=7, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=7, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.03, max_depth=7, n_estimators=1000; total time=   1.6s\n",
      "[CV] END .learning_rate=0.03, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END .learning_rate=0.03, max_depth=7, n_estimators=1000; total time=   1.8s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=500; total time=   0.3s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=3, n_estimators=800; total time=   0.6s\n",
      "[CV] END .learning_rate=0.05, max_depth=3, n_estimators=1000; total time=   0.7s\n",
      "[CV] END .learning_rate=0.05, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.05, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.05, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.05, max_depth=3, n_estimators=1000; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=800; total time=   0.5s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=800; total time=   0.9s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.05, max_depth=5, n_estimators=800; total time=   1.0s\n",
      "[CV] END .learning_rate=0.05, max_depth=5, n_estimators=1000; total time=   0.6s\n",
      "[CV] END .learning_rate=0.05, max_depth=5, n_estimators=1000; total time=   1.0s\n",
      "[CV] END .learning_rate=0.05, max_depth=5, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.05, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END .learning_rate=0.05, max_depth=5, n_estimators=1000; total time=   1.3s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=500; total time=   0.4s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=500; total time=   0.6s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=500; total time=   0.8s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=800; total time=   0.5s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=800; total time=   0.7s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=800; total time=   1.0s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=800; total time=   1.1s\n",
      "[CV] END ..learning_rate=0.05, max_depth=7, n_estimators=800; total time=   1.3s\n",
      "[CV] END .learning_rate=0.05, max_depth=7, n_estimators=1000; total time=   0.5s\n",
      "[CV] END .learning_rate=0.05, max_depth=7, n_estimators=1000; total time=   0.8s\n",
      "[CV] END .learning_rate=0.05, max_depth=7, n_estimators=1000; total time=   1.1s\n",
      "[CV] END .learning_rate=0.05, max_depth=7, n_estimators=1000; total time=   1.2s\n",
      "[CV] END .learning_rate=0.05, max_depth=7, n_estimators=1000; total time=   1.5s\n"
     ]
    },
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-1 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-1 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-1 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-1 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-1 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-1 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-1 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-1 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-1 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-1 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-1 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-1 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-1 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-1 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-1\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False,\n",
       "                                    eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_polic...\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=1000,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.03, 0.05],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [500, 800, 1000]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item sk-dashed-wrapped\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-1\" type=\"checkbox\" ><label for=\"sk-estimator-id-1\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;&nbsp;GridSearchCV<a class=\"sk-estimator-doc-link fitted\" rel=\"noreferrer\" target=\"_blank\" href=\"https://scikit-learn.org/1.4/modules/generated/sklearn.model_selection.GridSearchCV.html\">?<span>Documentation for GridSearchCV</span></a><span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False,\n",
       "                                    eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_polic...\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=1000,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={&#x27;learning_rate&#x27;: [0.01, 0.03, 0.05],\n",
       "                         &#x27;max_depth&#x27;: [3, 5, 7],\n",
       "                         &#x27;n_estimators&#x27;: [500, 800, 1000]},\n",
       "             scoring=&#x27;neg_mean_squared_error&#x27;, verbose=2)</pre></div> </div></div><div class=\"sk-parallel\"><div class=\"sk-parallel-item\"><div class=\"sk-item\"><div class=\"sk-label-container\"><div class=\"sk-label fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-2\" type=\"checkbox\" ><label for=\"sk-estimator-id-2\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">estimator: XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "             feature_types=None, gamma=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.03, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, multi_strategy=None, n_estimators=1000,\n",
       "             n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div><div class=\"sk-serial\"><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-3\" type=\"checkbox\" ><label for=\"sk-estimator-id-3\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">XGBRegressor</label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=None,\n",
       "             enable_categorical=False, eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "             feature_types=None, gamma=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.03, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=None,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, multi_strategy=None, n_estimators=1000,\n",
       "             n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div></div></div></div></div></div>"
      ],
      "text/plain": [
       "GridSearchCV(cv=TimeSeriesSplit(gap=0, max_train_size=None, n_splits=5, test_size=None),\n",
       "             estimator=XGBRegressor(base_score=None, booster=None,\n",
       "                                    callbacks=None, colsample_bylevel=None,\n",
       "                                    colsample_bynode=None,\n",
       "                                    colsample_bytree=None, device=None,\n",
       "                                    early_stopping_rounds=None,\n",
       "                                    enable_categorical=False,\n",
       "                                    eval_metric=['mae', 'rmse'],\n",
       "                                    feature_types=None, gamma=None,\n",
       "                                    grow_polic...\n",
       "                                    max_cat_to_onehot=None, max_delta_step=None,\n",
       "                                    max_depth=None, max_leaves=None,\n",
       "                                    min_child_weight=None, missing=nan,\n",
       "                                    monotone_constraints=None,\n",
       "                                    multi_strategy=None, n_estimators=1000,\n",
       "                                    n_jobs=None, num_parallel_tree=None,\n",
       "                                    random_state=None, ...),\n",
       "             param_grid={'learning_rate': [0.01, 0.03, 0.05],\n",
       "                         'max_depth': [3, 5, 7],\n",
       "                         'n_estimators': [500, 800, 1000]},\n",
       "             scoring='neg_mean_squared_error', verbose=2)"
      ]
     },
     "execution_count": 12,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 时序交叉验证划分\n",
    "tscv = TimeSeriesSplit(n_splits=5)\n",
    "\n",
    "# 使用GridSearchCV进行超参数调优\n",
    "grid_search = GridSearchCV(\n",
    "    estimator=model,\n",
    "    param_grid=param_grid,\n",
    "    scoring='neg_mean_squared_error',  # 根据需要可更改评估指标，这里以均方误差为例\n",
    "    cv=tscv,\n",
    "    verbose=2\n",
    ")\n",
    "\n",
    "# 执行超参数搜索，传入完整的数据集X和y\n",
    "grid_search.fit(\n",
    "    X, y,\n",
    "    verbose=False,  # 设置 verbose=False\n",
    ")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 13,
   "id": "506d6c38-d246-4d8d-9811-b2189b9dcab2",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Best Hyperparameters: {'learning_rate': 0.01, 'max_depth': 3, 'n_estimators': 800}\n",
      "Best Model CV Score: 0.011451393913332147\n"
     ]
    }
   ],
   "source": [
    "# 输出最佳超参数组合\n",
    "print(\"Best Hyperparameters:\", grid_search.best_params_)\n",
    "\n",
    "# 输出最佳模型在交叉验证中的平均得分（根据选择的评估指标）\n",
    "print(\"Best Model CV Score:\", -grid_search.best_score_)"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 14,
   "id": "124a1fb2-ba59-48b2-9a4f-def91e9871e5",
   "metadata": {},
   "outputs": [],
   "source": [
    "# 输出最佳模型\n",
    "best_model = grid_search.best_estimator_"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 15,
   "id": "180a331f-c7ab-4ece-8b00-bc7a6e39edb1",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "text/html": [
       "<style>#sk-container-id-2 {\n",
       "  /* Definition of color scheme common for light and dark mode */\n",
       "  --sklearn-color-text: black;\n",
       "  --sklearn-color-line: gray;\n",
       "  /* Definition of color scheme for unfitted estimators */\n",
       "  --sklearn-color-unfitted-level-0: #fff5e6;\n",
       "  --sklearn-color-unfitted-level-1: #f6e4d2;\n",
       "  --sklearn-color-unfitted-level-2: #ffe0b3;\n",
       "  --sklearn-color-unfitted-level-3: chocolate;\n",
       "  /* Definition of color scheme for fitted estimators */\n",
       "  --sklearn-color-fitted-level-0: #f0f8ff;\n",
       "  --sklearn-color-fitted-level-1: #d4ebff;\n",
       "  --sklearn-color-fitted-level-2: #b3dbfd;\n",
       "  --sklearn-color-fitted-level-3: cornflowerblue;\n",
       "\n",
       "  /* Specific color for light theme */\n",
       "  --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, white)));\n",
       "  --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, black)));\n",
       "  --sklearn-color-icon: #696969;\n",
       "\n",
       "  @media (prefers-color-scheme: dark) {\n",
       "    /* Redefinition of color scheme for dark theme */\n",
       "    --sklearn-color-text-on-default-background: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-background: var(--sg-background-color, var(--theme-background, var(--jp-layout-color0, #111)));\n",
       "    --sklearn-color-border-box: var(--sg-text-color, var(--theme-code-foreground, var(--jp-content-font-color1, white)));\n",
       "    --sklearn-color-icon: #878787;\n",
       "  }\n",
       "}\n",
       "\n",
       "#sk-container-id-2 {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 pre {\n",
       "  padding: 0;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-hidden--visually {\n",
       "  border: 0;\n",
       "  clip: rect(1px 1px 1px 1px);\n",
       "  clip: rect(1px, 1px, 1px, 1px);\n",
       "  height: 1px;\n",
       "  margin: -1px;\n",
       "  overflow: hidden;\n",
       "  padding: 0;\n",
       "  position: absolute;\n",
       "  width: 1px;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-dashed-wrapped {\n",
       "  border: 1px dashed var(--sklearn-color-line);\n",
       "  margin: 0 0.4em 0.5em 0.4em;\n",
       "  box-sizing: border-box;\n",
       "  padding-bottom: 0.4em;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-container {\n",
       "  /* jupyter's `normalize.less` sets `[hidden] { display: none; }`\n",
       "     but bootstrap.min.css set `[hidden] { display: none !important; }`\n",
       "     so we also need the `!important` here to be able to override the\n",
       "     default hidden behavior on the sphinx rendered scikit-learn.org.\n",
       "     See: https://github.com/scikit-learn/scikit-learn/issues/21755 */\n",
       "  display: inline-block !important;\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-text-repr-fallback {\n",
       "  display: none;\n",
       "}\n",
       "\n",
       "div.sk-parallel-item,\n",
       "div.sk-serial,\n",
       "div.sk-item {\n",
       "  /* draw centered vertical line to link estimators */\n",
       "  background-image: linear-gradient(var(--sklearn-color-text-on-default-background), var(--sklearn-color-text-on-default-background));\n",
       "  background-size: 2px 100%;\n",
       "  background-repeat: no-repeat;\n",
       "  background-position: center center;\n",
       "}\n",
       "\n",
       "/* Parallel-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item::after {\n",
       "  content: \"\";\n",
       "  width: 100%;\n",
       "  border-bottom: 2px solid var(--sklearn-color-text-on-default-background);\n",
       "  flex-grow: 1;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel {\n",
       "  display: flex;\n",
       "  align-items: stretch;\n",
       "  justify-content: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  position: relative;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:first-child::after {\n",
       "  align-self: flex-end;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:last-child::after {\n",
       "  align-self: flex-start;\n",
       "  width: 50%;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-parallel-item:only-child::after {\n",
       "  width: 0;\n",
       "}\n",
       "\n",
       "/* Serial-specific style estimator block */\n",
       "\n",
       "#sk-container-id-2 div.sk-serial {\n",
       "  display: flex;\n",
       "  flex-direction: column;\n",
       "  align-items: center;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  padding-right: 1em;\n",
       "  padding-left: 1em;\n",
       "}\n",
       "\n",
       "\n",
       "/* Toggleable style: style used for estimator/Pipeline/ColumnTransformer box that is\n",
       "clickable and can be expanded/collapsed.\n",
       "- Pipeline and ColumnTransformer use this feature and define the default style\n",
       "- Estimators will overwrite some part of the style using the `sk-estimator` class\n",
       "*/\n",
       "\n",
       "/* Pipeline and ColumnTransformer style (default) */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable {\n",
       "  /* Default theme specific background. It is overwritten whether we have a\n",
       "  specific estimator or a Pipeline/ColumnTransformer */\n",
       "  background-color: var(--sklearn-color-background);\n",
       "}\n",
       "\n",
       "/* Toggleable label */\n",
       "#sk-container-id-2 label.sk-toggleable__label {\n",
       "  cursor: pointer;\n",
       "  display: block;\n",
       "  width: 100%;\n",
       "  margin-bottom: 0;\n",
       "  padding: 0.5em;\n",
       "  box-sizing: border-box;\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:before {\n",
       "  /* Arrow on the left of the label */\n",
       "  content: \"▸\";\n",
       "  float: left;\n",
       "  margin-right: 0.25em;\n",
       "  color: var(--sklearn-color-icon);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 label.sk-toggleable__label-arrow:hover:before {\n",
       "  color: var(--sklearn-color-text);\n",
       "}\n",
       "\n",
       "/* Toggleable content - dropdown */\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content {\n",
       "  max-height: 0;\n",
       "  max-width: 0;\n",
       "  overflow: hidden;\n",
       "  text-align: left;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content pre {\n",
       "  margin: 0.2em;\n",
       "  border-radius: 0.25em;\n",
       "  color: var(--sklearn-color-text);\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-toggleable__content.fitted pre {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~div.sk-toggleable__content {\n",
       "  /* Expand drop-down */\n",
       "  max-height: 200px;\n",
       "  max-width: 100%;\n",
       "  overflow: auto;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 input.sk-toggleable__control:checked~label.sk-toggleable__label-arrow:before {\n",
       "  content: \"▾\";\n",
       "}\n",
       "\n",
       "/* Pipeline/ColumnTransformer-specific style */\n",
       "\n",
       "#sk-container-id-2 div.sk-label input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator-specific style */\n",
       "\n",
       "/* Colorize estimator box */\n",
       "#sk-container-id-2 div.sk-estimator input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted input.sk-toggleable__control:checked~label.sk-toggleable__label {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label label.sk-toggleable__label,\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  /* The background is the default theme color */\n",
       "  color: var(--sklearn-color-text-on-default-background);\n",
       "}\n",
       "\n",
       "/* On hover, darken the color of the background */\n",
       "#sk-container-id-2 div.sk-label:hover label.sk-toggleable__label {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "/* Label box, darken color on hover, fitted */\n",
       "#sk-container-id-2 div.sk-label.fitted:hover label.sk-toggleable__label.fitted {\n",
       "  color: var(--sklearn-color-text);\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Estimator label */\n",
       "\n",
       "#sk-container-id-2 div.sk-label label {\n",
       "  font-family: monospace;\n",
       "  font-weight: bold;\n",
       "  display: inline-block;\n",
       "  line-height: 1.2em;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-label-container {\n",
       "  text-align: center;\n",
       "}\n",
       "\n",
       "/* Estimator-specific */\n",
       "#sk-container-id-2 div.sk-estimator {\n",
       "  font-family: monospace;\n",
       "  border: 1px dotted var(--sklearn-color-border-box);\n",
       "  border-radius: 0.25em;\n",
       "  box-sizing: border-box;\n",
       "  margin-bottom: 0.5em;\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-0);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-0);\n",
       "}\n",
       "\n",
       "/* on hover */\n",
       "#sk-container-id-2 div.sk-estimator:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-2);\n",
       "}\n",
       "\n",
       "#sk-container-id-2 div.sk-estimator.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-2);\n",
       "}\n",
       "\n",
       "/* Specification for estimator info (e.g. \"i\" and \"?\") */\n",
       "\n",
       "/* Common style for \"i\" and \"?\" */\n",
       "\n",
       ".sk-estimator-doc-link,\n",
       "a:link.sk-estimator-doc-link,\n",
       "a:visited.sk-estimator-doc-link {\n",
       "  float: right;\n",
       "  font-size: smaller;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1em;\n",
       "  height: 1em;\n",
       "  width: 1em;\n",
       "  text-decoration: none !important;\n",
       "  margin-left: 1ex;\n",
       "  /* unfitted */\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted,\n",
       "a:link.sk-estimator-doc-link.fitted,\n",
       "a:visited.sk-estimator-doc-link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "div.sk-estimator:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link:hover,\n",
       ".sk-estimator-doc-link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "div.sk-estimator.fitted:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover,\n",
       "div.sk-label-container:hover .sk-estimator-doc-link.fitted:hover,\n",
       ".sk-estimator-doc-link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "/* Span, style for the box shown on hovering the info icon */\n",
       ".sk-estimator-doc-link span {\n",
       "  display: none;\n",
       "  z-index: 9999;\n",
       "  position: relative;\n",
       "  font-weight: normal;\n",
       "  right: .2ex;\n",
       "  padding: .5ex;\n",
       "  margin: .5ex;\n",
       "  width: min-content;\n",
       "  min-width: 20ex;\n",
       "  max-width: 50ex;\n",
       "  color: var(--sklearn-color-text);\n",
       "  box-shadow: 2pt 2pt 4pt #999;\n",
       "  /* unfitted */\n",
       "  background: var(--sklearn-color-unfitted-level-0);\n",
       "  border: .5pt solid var(--sklearn-color-unfitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link.fitted span {\n",
       "  /* fitted */\n",
       "  background: var(--sklearn-color-fitted-level-0);\n",
       "  border: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "\n",
       ".sk-estimator-doc-link:hover span {\n",
       "  display: block;\n",
       "}\n",
       "\n",
       "/* \"?\"-specific style due to the `<a>` HTML tag */\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link {\n",
       "  float: right;\n",
       "  font-size: 1rem;\n",
       "  line-height: 1em;\n",
       "  font-family: monospace;\n",
       "  background-color: var(--sklearn-color-background);\n",
       "  border-radius: 1rem;\n",
       "  height: 1rem;\n",
       "  width: 1rem;\n",
       "  text-decoration: none;\n",
       "  /* unfitted */\n",
       "  color: var(--sklearn-color-unfitted-level-1);\n",
       "  border: var(--sklearn-color-unfitted-level-1) 1pt solid;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted {\n",
       "  /* fitted */\n",
       "  border: var(--sklearn-color-fitted-level-1) 1pt solid;\n",
       "  color: var(--sklearn-color-fitted-level-1);\n",
       "}\n",
       "\n",
       "/* On hover */\n",
       "#sk-container-id-2 a.estimator_doc_link:hover {\n",
       "  /* unfitted */\n",
       "  background-color: var(--sklearn-color-unfitted-level-3);\n",
       "  color: var(--sklearn-color-background);\n",
       "  text-decoration: none;\n",
       "}\n",
       "\n",
       "#sk-container-id-2 a.estimator_doc_link.fitted:hover {\n",
       "  /* fitted */\n",
       "  background-color: var(--sklearn-color-fitted-level-3);\n",
       "}\n",
       "</style><div id=\"sk-container-id-2\" class=\"sk-top-container\"><div class=\"sk-text-repr-fallback\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=10,\n",
       "             enable_categorical=False, eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "             feature_types=None, gamma=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, multi_strategy=None, n_estimators=800,\n",
       "             n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre><b>In a Jupyter environment, please rerun this cell to show the HTML representation or trust the notebook. <br />On GitHub, the HTML representation is unable to render, please try loading this page with nbviewer.org.</b></div><div class=\"sk-container\" hidden><div class=\"sk-item\"><div class=\"sk-estimator fitted sk-toggleable\"><input class=\"sk-toggleable__control sk-hidden--visually\" id=\"sk-estimator-id-4\" type=\"checkbox\" checked><label for=\"sk-estimator-id-4\" class=\"sk-toggleable__label fitted sk-toggleable__label-arrow fitted\">&nbsp;XGBRegressor<span class=\"sk-estimator-doc-link fitted\">i<span>Fitted</span></span></label><div class=\"sk-toggleable__content fitted\"><pre>XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=10,\n",
       "             enable_categorical=False, eval_metric=[&#x27;mae&#x27;, &#x27;rmse&#x27;],\n",
       "             feature_types=None, gamma=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, multi_strategy=None, n_estimators=800,\n",
       "             n_jobs=None, num_parallel_tree=None, random_state=None, ...)</pre></div> </div></div></div></div>"
      ],
      "text/plain": [
       "XGBRegressor(base_score=None, booster=None, callbacks=None,\n",
       "             colsample_bylevel=None, colsample_bynode=None,\n",
       "             colsample_bytree=None, device=None, early_stopping_rounds=10,\n",
       "             enable_categorical=False, eval_metric=['mae', 'rmse'],\n",
       "             feature_types=None, gamma=None, grow_policy=None,\n",
       "             importance_type=None, interaction_constraints=None,\n",
       "             learning_rate=0.01, max_bin=None, max_cat_threshold=None,\n",
       "             max_cat_to_onehot=None, max_delta_step=None, max_depth=3,\n",
       "             max_leaves=None, min_child_weight=None, missing=nan,\n",
       "             monotone_constraints=None, multi_strategy=None, n_estimators=800,\n",
       "             n_jobs=None, num_parallel_tree=None, random_state=None, ...)"
      ]
     },
     "execution_count": 15,
     "metadata": {},
     "output_type": "execute_result"
    }
   ],
   "source": [
    "# 设置早停轮数，如果验证集的性能在连续10轮内没有提升，则提前停止训练\n",
    "best_model.set_params(early_stopping_rounds=10)"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "9437e102-9fb0-42b9-9b4a-e997e0f9f418",
   "metadata": {},
   "source": [
    "**交叉验证与模型评估**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 16,
   "id": "532bd758-5ca8-4652-a916-6825395887a8",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "Fold 0 - Mean Absolute Error: 0.1745\n",
      "Fold 0 - Mean Absolute Percentage Error: 27.5793%\n",
      "Fold 0 - Mean Squared Error: 0.0407\n",
      "Fold 0 - Root Mean Squared Error: 0.2017\n",
      "Fold 0 - R²: -1.9179\n",
      "\n",
      "Fold 1 - Mean Absolute Error: 0.0381\n",
      "Fold 1 - Mean Absolute Percentage Error: 13.3691%\n",
      "Fold 1 - Mean Squared Error: 0.0024\n",
      "Fold 1 - Root Mean Squared Error: 0.0487\n",
      "Fold 1 - R²: 0.8791\n",
      "\n",
      "Fold 2 - Mean Absolute Error: 0.0286\n",
      "Fold 2 - Mean Absolute Percentage Error: 13.9166%\n",
      "Fold 2 - Mean Squared Error: 0.0015\n",
      "Fold 2 - Root Mean Squared Error: 0.0382\n",
      "Fold 2 - R²: 0.9272\n",
      "\n",
      "Fold 3 - Mean Absolute Error: 0.0738\n",
      "Fold 3 - Mean Absolute Percentage Error: 8.6681%\n",
      "Fold 3 - Mean Squared Error: 0.0114\n",
      "Fold 3 - Root Mean Squared Error: 0.1068\n",
      "Fold 3 - R²: -0.0662\n",
      "\n",
      "Fold 4 - Mean Absolute Error: 0.0242\n",
      "Fold 4 - Mean Absolute Percentage Error: 3.1247%\n",
      "Fold 4 - Mean Squared Error: 0.0010\n",
      "Fold 4 - Root Mean Squared Error: 0.0321\n",
      "Fold 4 - R²: 0.9339\n",
      "\n"
     ]
    }
   ],
   "source": [
    "# 基于交叉验证的数据集划分\n",
    "for i, (train_index, test_index) in enumerate(tscv.split(X)):\n",
    "    X_train, X_test = X[train_index], X[test_index]\n",
    "    y_train, y_test = y[train_index], y[test_index]\n",
    "    \n",
    "    # 模型训练\n",
    "    best_model.fit(\n",
    "        X_train, y_train,\n",
    "        eval_set=[(X_train, y_train), (X_test, y_test)], \n",
    "        verbose=False,  # 设置 verbose=True 以在训练过程中打印评估结果\n",
    "        )\n",
    "    # 模型预测\n",
    "    y_pred = best_model.predict(X_test)\n",
    "\n",
    "    # 模型评估\n",
    "    mae = mean_absolute_error(y_test, y_pred)\n",
    "    print(f\"Fold {i} - Mean Absolute Error: {mae:.4f}\")\n",
    "\n",
    "    mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "    print(f\"Fold {i} - Mean Absolute Percentage Error: {mape * 100:.4f}%\")\n",
    "    \n",
    "    mse = mean_squared_error(y_test, y_pred)\n",
    "    print(f\"Fold {i} - Mean Squared Error: {mse:.4f}\")\n",
    "\n",
    "    rmse = root_mean_squared_error(y_test, y_pred)\n",
    "    print(f\"Fold {i} - Root Mean Squared Error: {rmse:.4f}\")\n",
    "\n",
    "    r2 = r2_score(y_test, y_pred)\n",
    "    print(f\"Fold {i} - R²: {r2:.4f}\\n\")"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "5c6e08a9-cc99-4e35-907f-06b6223ab6e6",
   "metadata": {},
   "source": [
    "通过以上每一个 `Fold` 的得分，我们可以清晰观察到最佳 `Fold` 为 `Fold 4`。`MSE` 得分越低，`R²` 得分越高越好"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 17,
   "id": "277e9521-ac6f-4c93-913e-6be622f2014b",
   "metadata": {},
   "outputs": [],
   "source": [
    "all_splits = list(tscv.split(X, y))\n",
    "train_idx, test_idx = all_splits[4]\n",
    "X_train, X_test = X[train_idx, :], X[test_idx, :]\n",
    "y_train, y_test = y[train_idx], y[test_idx]"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "c7eb2310-eb47-4947-8fb5-7f11d4656b8e",
   "metadata": {},
   "source": [
    "**验证损失曲线**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "7dc270ec-b7c7-4661-a8d9-a3e67e3a0b6e",
   "metadata": {},
   "source": [
    "模型验证损失"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 18,
   "id": "5d2e9677-2f3a-42e5-a568-522457ab1e7f",
   "metadata": {},
   "outputs": [],
   "source": [
    "mae_train = best_model.evals_result()['validation_0']['mae']\n",
    "rmse_train = best_model.evals_result()['validation_0']['rmse']\n",
    "mae_test = best_model.evals_result()['validation_1']['mae']\n",
    "rmse_test = best_model.evals_result()['validation_1']['rmse']"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "63caafed-b7f2-461a-a91b-6b0d3c0ef5d1",
   "metadata": {},
   "source": [
    "绘制 Fold 4 的验证损失曲线"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 19,
   "id": "dcc8752d-79b7-449c-a61e-5227e730ef04",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1800x600 with 2 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "fig, ax = plt.subplots(ncols=2, nrows=1, \n",
    "                       figsize=(18, 6))\n",
    "# MAE\n",
    "ax[0].plot(mae_train, label='MAE Train')\n",
    "ax[0].plot(mae_test, label='MAE Test')\n",
    "ax[0].set_title('MAE Loss Curve',\n",
    "                fontdict={'family': 'Times New Roman', \n",
    "                          'fontsize': 16, \n",
    "                          'fontweight': 'bold', \n",
    "                          'color': 'blue'})\n",
    "ax[0].set_xlabel('Iterations',\n",
    "                 fontdict={'family': 'Times New Roman', \n",
    "                           'fontsize': 14})\n",
    "ax[0].set_ylabel('MAE',\n",
    "                 fontdict={'family': 'Times New Roman', \n",
    "                           'fontsize': 14})\n",
    "ax[0].legend(prop={'family': 'Times New Roman'})\n",
    "# RMSE\n",
    "ax[1].plot(rmse_train, label='RMSE Train')\n",
    "ax[1].plot(rmse_test, label='RMSE Test')\n",
    "ax[1].set_title('RMSE Loss Curve', \n",
    "                fontdict={'family': 'Times New Roman', \n",
    "                          'fontsize': 16, \n",
    "                          'fontweight': 'bold', \n",
    "                          'color': 'blue'})\n",
    "ax[1].set_xlabel('Iterations', \n",
    "                 fontdict={'family': 'Times New Roman', \n",
    "                           'fontsize': 14})\n",
    "ax[1].set_ylabel('RMSE',\n",
    "                 fontdict={'family': 'Times New Roman', \n",
    "                           'fontsize': 14})\n",
    "ax[1].legend(prop={'family': 'Times New Roman'})\n",
    "\n",
    "# 调整子图之间的间距\n",
    "plt.tight_layout()\n",
    "\n",
    "# 显示图形\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "df2eb7f7-6c68-41a7-a1b1-9ff4c49322c7",
   "metadata": {},
   "source": [
    "**验证集预测**"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 20,
   "id": "d6273b92-da40-45d9-8049-0e31272eef9f",
   "metadata": {},
   "outputs": [],
   "source": [
    "y_test_denormalized = scaler.inverse_transform(y_test.reshape(1, -1)).ravel()\n",
    "y_pred_denormalized = scaler.inverse_transform(y_pred.reshape(1, -1)).ravel()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 21,
   "id": "dd28b217-69d6-49b9-9e42-5b12da161814",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 1000x600 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(10,6))\n",
    "\n",
    "plt.plot(y_test_denormalized, label='Actual Values')\n",
    "plt.plot(y_pred_denormalized, label='Predicted Values')\n",
    "\n",
    "plt.title('Comparison of validation set prediction results', \n",
    "          fontdict={'family': 'Times New Roman', \n",
    "          'fontsize': 16, 'fontweight': 'bold', 'color': 'blue'})\n",
    "plt.ylabel('Close Price USD ($)', fontdict={'family': 'Times New Roman', 'fontsize': 14})\n",
    "plt.legend(prop={'family': 'Times New Roman'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 22,
   "id": "fa048227-6f8f-4084-a400-6b75324dab01",
   "metadata": {},
   "outputs": [
    {
     "data": {
      "image/png": "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",
      "text/plain": [
       "<Figure size 500x500 with 1 Axes>"
      ]
     },
     "metadata": {},
     "output_type": "display_data"
    }
   ],
   "source": [
    "plt.figure(figsize=(5, 5), dpi=100)\n",
    "sns.regplot(x=y_test_denormalized, y=y_pred_denormalized, scatter=True, marker=\"*\", color='gold',line_kws={'color': 'red'})\n",
    "plt.show()"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "02c2a77b-921c-4007-aa36-b3a781a99a59",
   "metadata": {},
   "source": [
    "**评估指标**"
   ]
  },
  {
   "cell_type": "markdown",
   "id": "49242ad7-b1fa-495f-a155-3f57f5b2d8d9",
   "metadata": {},
   "source": [
    "通过调用 `sklearn.metrics` 模块中的 `mean_absolute_error` `mean_absolute_percentage_error` `mean_squared_error` `root_mean_squared_error` `r2_score` 函数来评估模型性能"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": 23,
   "id": "0f6b2984-df04-45c6-879c-bff640996c84",
   "metadata": {},
   "outputs": [
    {
     "name": "stdout",
     "output_type": "stream",
     "text": [
      "MAE: 0.0242\n",
      "MAPE: 3.1247%\n",
      "MSE: 0.0010\n",
      "RMSE: 0.0321\n",
      "R²: 0.9339\n"
     ]
    }
   ],
   "source": [
    "mae = mean_absolute_error(y_test, y_pred)\n",
    "print(f\"MAE: {mae:.4f}\")\n",
    "\n",
    "mape = mean_absolute_percentage_error(y_test, y_pred)\n",
    "print(f\"MAPE: {mape * 100:.4f}%\")\n",
    "\n",
    "mse = mean_squared_error(y_test, y_pred)\n",
    "print(f\"MSE: {mse:.4f}\")\n",
    "\n",
    "rmse = root_mean_squared_error(y_test, y_pred)\n",
    "print(f\"RMSE: {rmse:.4f}\")\n",
    "\n",
    "r2 = r2_score(y_test, y_pred)\n",
    "print(f\"R²: {r2:.4f}\")"
   ]
  },
  {
   "cell_type": "code",
   "execution_count": null,
   "id": "da5f02f4-1cac-4d92-b869-ee535945609c",
   "metadata": {},
   "outputs": [],
   "source": []
  }
 ],
 "metadata": {
  "kernelspec": {
   "display_name": "Python 3 (ipykernel)",
   "language": "python",
   "name": "python3"
  },
  "language_info": {
   "codemirror_mode": {
    "name": "ipython",
    "version": 3
   },
   "file_extension": ".py",
   "mimetype": "text/x-python",
   "name": "python",
   "nbconvert_exporter": "python",
   "pygments_lexer": "ipython3",
   "version": "3.12.2"
  }
 },
 "nbformat": 4,
 "nbformat_minor": 5
}
